IDEAS home Printed from https://ideas.repec.org/a/jcs/journl/v1y2017i3p1-16.html
   My bibliography  Save this article

Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data

Author

Listed:
  • Rodolfo C. Raga Jr.

    (Jose Rizal University)

  • Jennifer D. Raga

    (Western Group of Companies)

Abstract

Purpose: This paper proposes a novel approach for processing course log data obtained from Moodle-based blended courses in order to visualize patterns of student activity within the online environment and to determine whether these log data can be used to predict student academic performance. Method: Logs of student activities were summarized and processed using the Vector Space Model approach. This resulted in a novel vector-based form of representation which can be used to map students activity in a latent activity space given a set of activity dimensions. An enriched form of this representation was also generated by processing the Date, Time, and IP address metadata for the purpose of developing classification/predictive model of students performance. Results: The activity space coupled with a one-hot vector representation for each unique activity dimension can be used to visualize the differences in level and type of activity preferences of students. Experiments using several machine learning algorithms indicate that the generated model can modestly distinguish between sets of activities that lead to High, Low, or Failed performances. Conclusion: The development of easily interpretable graphics that can depict trends in student activity is a useful tool for instructors handling blended courses. It can provide constant monitoring of course progression with minimal effort and enable instructors to determine whether and how the environment actually affects student performance. Recommendations: Further work on refining the process applied to the data is recommended. The log data should be time-sliced and processed to determine whether and how the students level and type of activity changes over time. More powerful machine learning classification techniques shouldalso be tested to determine whether it can improve the classification accuracy. Research Implications: These types of visualizations and predictive models could be used to monitor the student or class which requires immediate and specific pedagogical adjustments.

Suggested Citation

  • Rodolfo C. Raga Jr. & Jennifer D. Raga, 2017. "Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data," International Journal of Computing Sciences Research, Step Academic, vol. 1(3), pages 1-16, December.
  • Handle: RePEc:jcs:journl:v:1:y:2017:i:3:p:1-16
    DOI: 10.25147/iijcsr.2017.001.1.09
    as

    Download full text from publisher

    File URL: https://www.stepacademic.net/ijcsr/article/view/33/19
    Download Restriction: no

    File URL: https://libkey.io/10.25147/iijcsr.2017.001.1.09?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jcs:journl:v:1:y:2017:i:3:p:1-16. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Liam Demafelix (email available below). General contact details of provider: https://www.stepacademic.net .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.